A Novel Genetic Grey Wolf optimizer for Global optimization and Feature Selection

B. Kihel, S. Chouraqui
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引用次数: 3

Abstract

In this paper, a new stochastic search strategy inspired by the Grey Wolf optimization theory is proposed for feature subset selection. Grey Wolf optimization algorithm (GWO) is a new metaheuristic optimization technique. Its principle is to reproduce the behavior of grey wolves in nature to hunt in a cooperative way. In this work, we have used the Grey Wolf optimizer and Genetic algorithm to select the most relevant features in a dataset. Then we have proposed a new Genetic Grey Wolf optimization algorithm. In our proposed strategy, feature selection algorithm is formulated as an optimization problem that searches an optimum with less number of features in a feature space and a good accuracy. The goal of our study is to achieve a balance between the classification accuracy and the size of the feature subsets selected. Our proposed approach has been evaluated on 10 standard datasets taken from UCI repository and validated on 02 big datasets used in literature. The experimental results show the superiority of GWO algorithm in classification performance and dimensionality reduction.
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一种用于全局优化和特征选择的遗传灰狼优化器
本文在灰狼优化理论的启发下,提出了一种新的特征子集选择随机搜索策略。灰狼优化算法是一种新的元启发式优化技术。其原理是复制自然界灰狼的行为,以合作的方式狩猎。在这项工作中,我们使用灰狼优化器和遗传算法来选择数据集中最相关的特征。在此基础上提出了一种新的遗传灰狼优化算法。在我们提出的策略中,特征选择算法被描述为一个优化问题,即在特征空间中搜索特征数量较少且精度较高的最优。我们研究的目标是在分类精度和所选特征子集的大小之间取得平衡。我们提出的方法已经在来自UCI存储库的10个标准数据集上进行了评估,并在文献中使用的02个大数据集上进行了验证。实验结果表明了GWO算法在分类性能和降维方面的优越性。
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